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Neural Computing and Applications
Erschienen in: Neural Computing and Applications 7/2016

01.10.2016 | Original Article

Stability in distribution of stochastic delay recurrent neural networks with Markovian switching

verfasst von: Enwen Zhu, George Yin, Quan Yuan

Erschienen in: Neural Computing and Applications | Ausgabe 7/2016

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Abstract

This paper investigates the stability in distribution of stochastic delay recurrent neural networks with Markovian switching. Using Lyapunov function and stochastic analysis techniques, sufficient conditions on the stability in distribution are given. For such recurrent neural networks, it reveals that the limit distribution of transition probability for segment process associated with solution process is indeed a unique ergodic invariant probability measure. Moreover, a numerical example is also provided to demonstrate the effectiveness and applicability of the theoretical results.
Vorheriger Artikel A hybrid functional link dynamic neural network and evolutionary unscented Kalman filter for short-term electricity price forecasting

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Metadaten
Titel
Stability in distribution of stochastic delay recurrent neural networks with Markovian switching
verfasst von
Enwen Zhu
George Yin
Quan Yuan
Publikationsdatum
01.10.2016
Verlag
Springer London
Erschienen in
Neural Computing and Applications / Ausgabe 7/2016
Print ISSN: 0941-0643
Elektronische ISSN: 1433-3058
DOI
https://doi.org/10.1007/s00521-015-2013-x

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